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The CMI Magnetocardiograph is intended for use as a tool that non-invasively measures and displays the magnetic signals produced by the electric currents in the heart.

This device integrates an array of magnetic detectors with data acquisition hardware/software for the purpose of measuring the magnetic signals generated by the electrical current flowing in the heart. The detectors are housed in a vertically adjustable holder. The patient bed moves horizontally in orthogonal directions allowing the acquisition of multiple datasets for different locations above the torso. Three standard ECG electrodes are placed on each wrist and one ankle of the subject to provide a reference signal for synchronization of multiple MCG datasets.

The MCG data is preprocessed and displayed as either real time traces, averaged traces, or as multi-dimensional color maps.

The CMI Magnetocardiograph's acceptance criteria and the study proving its compliance are detailed below. It's important to note that this 510(k) summary focuses on demonstrating substantial equivalence to predicate devices rather than establishing specific diagnostic performance metrics (e.g., sensitivity, specificity for a particular condition). Therefore, the "acceptance criteria" here relate to meeting the characteristics and safety standards of the predicate devices.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the CMI Magnetocardiograph are implicitly derived from its comparison to the predicate devices: the Hewlett Packard ECG (K760542) and the Neuromag-122 magnetoencephalograph (K962764). The device's performance is demonstrated through its functional similarity and achieved safety standards.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: The document mentions a "pilot study involving hundreds of subjects" for safety assessment. For the substantial equivalence claims regarding waveform morphology (ECG comparison) and functional equivalence (MEG comparison), it states "[c]linical data was also used as part of the indication for substantial equivalence to 5-lead ECG (Appendix H)" and "Appendix H presents data to support this claim [equivalence to MEG]." However, the exact number of subjects for these specific comparative data sets (used for the "test set" in a traditional algorithm performance sense) and their precise breakdown are not provided in the excerpt.
• Data Provenance: The document does not explicitly state the country of origin. It indicates "Extensive on-site testing at clinical sites" and "Data, taken from human subjects," which implies prospective collection at those clinical sites. It does not specify whether the data was retrospective or prospective beyond the implication of "clinical sites" and "hundreds of subjects" in a pilot study.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

The document does not provide information on the number or qualifications of experts used to establish a "ground truth" for the test set in the context of diagnostic performance (e.g., identifying specific heart conditions). The primary "ground truth" for this 510(k) is the established and accepted performance and safety of the predicate devices. The study aims to show the CMI Magnetocardiograph produces similar waveforms to an ECG and uses similar technology to an MEG.

4. Adjudication Method for the Test Set

The document does not describe an adjudication method for a test set in the context of diagnostic performance. The evaluation is based on comparing device characteristics and observed waveforms.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size of Human Reader Improvement

No. The document does not describe a MRMC comparative effectiveness study, nor does it discuss human reader improvement with or without AI assistance. This submission focuses on the standalone device performing similar to predicate devices, not on human-AI interaction or diagnostic aid.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes. The entire submission describes the performance of the device in a standalone capacity. It measures and displays magnetic signals. There is no mention of a human-in-the-loop component for its primary function in this submission relevant to its substantial equivalence claim. The device is a "tool which non-invasively measures and displays the magnetic signals produced by the electric currents in the heart."

7. The Type of Ground Truth Used

The "ground truth" for this 510(k) submission is primarily:

• Functional Characteristics and Safety of Predicate Devices: The CMI Magnetocardiograph is judged against the established characteristics, safety profiles, and intended uses of the Hewlett Packard ECG and the Neuromag-122 MEG.
• Expert Consensus on Waveform Morphology: For the ECG comparison, the "basis for equivalence...is that the CMI Magnetocardiograph produces magnetic waveforms (or traces) very similar in morphology to the electrical waveforms produced by the 5-lead Electrocardiograph." This implies an expert assessment or comparison of the visual similarity of these waveforms, though no specific expert panel is detailed.
• Engineering and Physics Principles: For the MEG comparison, the "basis for equivalence...is that the Magnetoencephalograph (MEG) predicate and the magnetocardiograph (MCG) both use very similar technology (SQUIDS) and both measure the magnetic field emanating from a source - they are functionally equivalent." This relies on established engineering principles and the known functionality of SQUID technology.

8. The Sample Size for the Training Set

The document does not mention a "training set" in the context of machine learning. The device described appears to be a hardware-based measurement system, not an AI/ML algorithm that undergoes a distinct training phase. Its software is tested for compliance, but not "trained" in the ML sense.

9. How the Ground Truth for the Training Set Was Established

As there is no explicit training set described for an AI/ML algorithm, this question is not applicable based on the provided document. The device's "training" in a broad sense would be its design, calibration, and engineering to measure physical phenomena accurately.

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